Project Summary In order to make an optimal value-based decision, an animal must effectively encode and compare the value ascribed to each choice. Wide swaths of cortex are implicated in integrating and representing the variables used to make a decision. However, the specificity and necessity of these areas to decision-making is an open area of study. Evidence suggests distinct, constituent elements of decision-making are encoded differentially across multiple brain areas. Reinforcement learning theory provides a framework to dissect these measurable decision variables from behavioral choice. Given both these decision variables and mesoscopic techniques to record the activity of individual neurons and their populations, the research proposed here aims to characterize the encoding of decision across multiple cortical areas. Using cutting-edge imaging and optogenetic tools, this proposed work will elucidate how ensembles of neurons in the brain compute value from recent experience during a probabilistic reward behavior paradigm. The encoding of value and other decision variables will then be tested with perturbation, interrupting normal activity to test causal function. The results of this work will provide insight to how the brain integrates information across cortical regions in order to reach a decision.